Small-aperture seismic arrays: instruments and detectability
ADVANCEMENT IN PROTEIN INFERENCE FROM SHOTGUN PROTEOMICS USING PEPTIDE DETECTABILITY PEDRO ALVES...
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Transcript of ADVANCEMENT IN PROTEIN INFERENCE FROM SHOTGUN PROTEOMICS USING PEPTIDE DETECTABILITY PEDRO ALVES...
ADVANCEMENT IN PROTEIN INFERENCE FROM SHOTGUNPROTEOMICS USING PEPTIDE
DETECTABILITY
PEDRO ALVES
Advisor: Predrag Radivojac
School of Informatics BLOOMINGTON
Overview
• Shotgun Proteomics
• Protein Inference Problem
• Protein Identification Using Peptide Detectability
• Results
• Limitations and Improvements
Degenerate Peptides
Rat Sample/Rat IPI Database60%
Nesvizhskii, A.I. and Aebersold, R. (2005) Interpretation of shotgun proteomic data: the protein inference problem. Mol. Cell Proteomics, 4, 1419–1440.
Protein Inference Problem
Solution 1 * (A, E)*
Solution 2 *
(B, C, D)
*
*
* *
Minimum Protein Set
11 Possible Solutions
Nesvizhskii, A.I. and Aebersold, R. (2005) Interpretation of shotgun proteomic data: the protein inference problem. Mol. Cell Proteomics, 4, 1419–1440.
Identified Peptides
1
2
3
4
5
6
7
8
9
10
Proteins
1 2 3 4 2 6
1 2 35 4 1 7 8
10 9 6
6 9
10
GMPSA
5
3 3
1 3
2
3
2 2
2
0
0
1
Greedy Minimum Protein Set Algorithm
Nesvizhskii, A.I. and Aebersold, R. (2005) Interpretation of shotgun proteomic data: the protein inference problem. Mol. Cell Proteomics, 4, 1419–1440.
Resolving Ambiguity
detectability of a peptide
– the probability that the peptide will be observed in a standard sample analyzed by a standard proteomics routine
Tang, H., Arnold, R. J., Alves, P., Xun, Z., Clemmer, D. E., Novotny, M. V., Reilly, J. P. & Radivojac, P. (2006). A computational approach toward label-free protein quantification using predicted peptide detectability. Bioinformatics, (2006) 22 (14): e481-e488
Factors affecting Peptide Detection
Four classes of factors
1) Chemical properties of the peptide (and parent protein)
2) Limitations of peptide identification protocol
3) Abundance of the peptide in the sample
4) Presence of other peptides that compete for detection
Mean Accuracy : 71%
Mean AUC : 78%
Synthetic : ~30% of peptides identified
Real : ~10% of peptides identified
Peptide Detectability Prediction
Identified Peptides
1
2
3
4
5
6
7
8
9
10
Proteins
Minimum Missed Peptides
12
45
4
2
6
1
4
5
7
8
6
9
27
10
24
53
23
17
55
6
9
10
14
1
17
2
3
1
2
15
3
24
01
Missed peptide
MDAP
Identified Peptides
1
2
3
4
5
6
7
8
9
10
ProteinsLDFA
12
45
4
2
6
1
4
5
7
8
6
9
27
10
24
53
23
17
55
6
9
10
1
4
5
7
6
2
9
8
10
3
14
1
17
2
3
1
2
15
3
24
2
1
0 2
10
RESULTS
GMPSA LDFA
Synthetic Sample
with 12 Proteins
7 correct proteins 10 correct
proteins
5 tied proteins
1 tied protein1 incorrect tied protein
GMPSA vs LDFAin a R. norvegicus sample
GMPSA LDFA
Rat Sample/Rat IPI Database
2346 94
Indistinguishable pairs
GMPSA vs LDFA
GMPSA LDFA
247 275
Total proteins identified
62% 81%
Percent of proteins assigned with no ties
153 224
Total assignments with no ties
149
Proteins assigned due to unique peptides
4 75
Total unambiguous assignments excluding the proteins with unique peptides
Identified Proteins
Unambiguously Identified Proteins
Limitations and Improvements
• Include missed-cleavage peptides
• Include lower scoring peptides to aid in the differentiation of tied proteins
• Include peptides identified with charges +1 and +3
• Train on other analytical platforms
• Study the effects of detectability prediction on algorithm results
Publications
• PSB 2007– Alves, P. , Arnold, R. , Novotny, M. , Radivojac, P. , Reilly, J. ,
Tang, H. (2007). Advancement in Protein Inference from Shotgun Proteomics Using Peptide Detectability. Pac. Symp. Biocomput., (2007) 12: 409-420
• ISMB 2006– Tang, H., Arnold, R. J., Alves, P., Xun, Z., Clemmer, D. E.,
Novotny, M. V., Reilly, J. P. & Radivojac, P. (2006). A computational approach toward label-free protein quantification using predicted peptide detectability. Bioinformatics, (2006) 22 (14): e481-e488.
Acknowledgements
• Predrag Radivojac
• Haixu Tang
• Randy Arnold
• IU School of Informatics
• IU Chemistry Dept.